Top-k closest-vertex queries on weighted knowledge graphs refer to the process of retrieving the k vertices that are closest to a given query vertex based on the shortest distance. This operation is particularly valuable in the Industrial Internet of Things (IIoT), where it leverages data security inversion and traceability such as risk identification, asset association analysis, and anomaly tracing across the entire data lifecycle. Although extensive research has been conducted on ranking and querying knowledge graphs, the specific problem of top-k closest-vertex queries on dynamic attributed knowledge graphs remains largely unexplored. To bridge this gap, we propose an attribute-based indexing mechanism, along with an associated scalable storage structure, to enable efficient top-k search and dynamic graph updates. We evaluate our approach in terms of update efficiency when new edges are added and query performance as k varies. Experimental results demonstrate that the update time scales linearly with the number of added edges, while the search time remains independent of k and is influenced only by the overall size of the knowledge graph.